The era of scaling pre-training may not be completely over, but reasoning now appears to offer the most potential for maximizing gains from scaling compute.

Despite increasing GPU resources, the intelligence improvements from the current generation of LLMs are not meeting expectations, with reports of failed training runs and diminishing returns emerging from major labs. Additionally, the lack of high-quality data to train new models has become a major bottleneck for AI development.

While scaling pre-training may have plateaued, training test time compute could open up a new paradigm for scaling laws, unlocking previously unimaginable capabilities.

In 2025, reasoning will become the defining focus in AI development, as pre-training reaches diminishing returns.

There's a significant gap between the current compute capabilities and what could be achieved with ten to a hundred times more compute, which would allow for much greater AI services.

Achieving AGI requires not just pre-training but also the ability to reason and utilize additional computational resources effectively to improve outcomes.

The advancements in AI depend significantly on the amount of computing power we allocate and the timing of unpredictable breakthroughs.

The future of computing will involve running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.

The AI field is approaching diminishing returns with large language models, as the amount of natural text data available for training is becoming limited, which may hinder future advancements.